مقایسهٔ روش‌ها

	آزمون کوانت-اندروز برای شکست‌های ساختاری ناشناخته	آزمون شکست ساختاری چندگانه بای-پرون	آزمون چاو برای گسست سازه‌ای
حوزه	اقتصادسنجی	اقتصادسنجی	اقتصادسنجی
خانواده	Hypothesis test	Hypothesis test	Regression model
سال پیدایش	1993	1998	1960
پدیدآور	Donald Andrews	Jushan Bai & Pierre Perron	Gregory C. Chow
نوع	Supremum test for structural change	Sequential hypothesis test for multiple structural breaks	Test for structural break in regression coefficients
منبع بنیادین	Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4), 821–856.	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605.
نام‌های دیگر	sup-Wald Test, Andrews Breakpoint Test, Unknown Structural Break Test, Quandt Likelihood Ratio Test	Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi	Chow breakpoint test, structural break test, Chow yapısal kırılma testi
مرتبط	3	2	2
خلاصه	The Quandt-Andrews test, formalized by Andrews (1993), detects structural breaks in regression parameters when the breakpoint date is unknown a priori. It sweeps all candidate break dates within a trimmed interior of the sample, computes a Wald (or LM/LR) statistic at each candidate, and reports the supremum of those statistics. Applied economists and time-series analysts use it to test whether coefficients remain stable across a full estimation window without needing to specify when the break occurred.	The Bai-Perron test, introduced by Jushan Bai and Pierre Perron in their landmark 1998 Econometrica paper, is a least-squares-based procedure for detecting, estimating, and testing the number of structural breaks in a linear regression model estimated on time-series data. Unlike single-break tests, it simultaneously identifies multiple change-points in a sample, providing economists and empirical researchers with a rigorous, data-driven way to locate parameter instability across time.	The Chow test, introduced by Gregory Chow in 1960, checks whether the coefficients of a linear regression are the same across two subsamples — that is, whether a structural break occurs at a known point such as a policy change, crisis, or regime shift. It compares the fit of a single pooled regression with the combined fit of two separate regressions; a large improvement from splitting indicates the relationship differs between the two periods or groups.